On Saturday, November 1, 2014 5:01:04 PM UTC-7, Francesco Bonazzi wrote:
>
> Apart from the bug fixes, it's quite hard to go on rigorously.
>
> You wrote you have some hackish stuff about derivatives, on github you can 
> create a gist, which is usually a piece of code unrelated to a project, 
> which people can see and comment.
>

I have made a gist with some code I wrote to do derivatives. 
https://gist.github.com/seniosh/ef0e1550110eec0c6b41#file-grtensor-py where 
can that then be discussed? I already realize several problems (partial 
differentiation gets treated as a Tensor, which is a problem if you want to 
switch coordinates), but it's a
starting point for discussion at least. 
 

> If you realized, the tensor module only allows expanded polynomial 
> expressions, that is difficultto change because the algorithm was written 
> for that, and supporting something different would require a llot of 
> rewrite.
>
> I have a pending PR on the tensor module, which is a major rewrite of the 
> code, but I'm not very satisfied of it.
>
Should I move discussion to that PR? Or should I make a new one?
 

> An alternative is to expose special objects such as partial derivatives to 
> the index structure of a tensor.
>
> If you like to do something, try to see how to create a covariant 
> derivative object that exposes the index structure.
>
> Say, CovarDerivative(i, A(j, k)) should be treated as a tensor of indices 
> i j k. You will realize that the way the tensor module was written makes 
> this task quite hard.
>

When I implemented it, my CovarDerivative function operated on data, and 
then returned a new TensMul object, so CovarDerivative(i, A(j, k)) returns 
dA(i, j, k).  

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